Most server placement literature [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]} does not account for workload uncertainty explicitly. In general, these server placement proposals take a predicted edge workload vector as input, and compute server placement solutions with optimal expenditure, optimal access delay, or minimal energy consumption. However, we cannot keep updating server placement based on the real-time workload. When the real-time workload patterns deviate from the predictions, the performance guarantee offered by these proposals become questionable. Some recent works [14]}, [15]}, [16]}, [17]} studied how to design edge server placement policies that are robust to server failures. However, workload uncertainty is inherently different from server failures, and thus may need completely new handling mechanisms.
